FOSRIN: Food security through ricebean research in India and Nepal

Work packages: WP3

Workpackages 2, 3 and 4 are all concerned with characterising the genetic resources available within Vigna umbellata in both Nepal and India, and collectively address project objective 2

To assess genetic diversity and indigenous knowledge on ricebean in Nepal and India.

There has been no systematic attempt to collect Nepalese ricebean germplasm and no systematic collection of indigenous technical knowledge regarding the crop in either country? We will assess the genetic diversity of ricebean in Nepal and India, collect and catalogue germplasm, and simultaneously with this will collect and document indigenous knowledge on the crop. We will use both field assessment and molecular characterisation of the material.

Workpackage 3 addresses objective 2.2:

To characterise germplasm diversity using molecular marker techniques

Random stratified sampling will be used to select a sub-set of accessions from the germplasm in WP2, and this will then be assayed for genetic marker diversity. First polymorphic SSR markers will be identified in a much more restricted set of ricebean germplasm selected to maximise geographic and phenotypic diversity. The selected sub-set of accessions will then be evaluated for diversity at these markers. The data will then be analysed for the number and frequency of each polymorphic SSR markers each having a specific number of alleles. The analysis of the relationships between genotypes will use both molecular data from this package and agromorphological data from WP4. It requires the construction of a matrix specifying the value for each trait (molecular or phenotypic) for each genotype. The traits are either continuous phenotypic variables (e.g., height, phenology) or discrete phenological variables (e.g., leaf or grain colour) or discrete genetic marker variables (normally absence or presence). Similarities or distances between all pairs of accessions are calculated to construct a matrix. For phenotypic data taxonomic distances such as squared Euclidean distance and the Mahalanobis squared distances are commonly used e.g. Martinello et al (2001); Ezeaku et al (1999); Ayana & Bekele (1999). Jacquard’s similarity coefficient, simple matching coefficient and Nei’s genetic distance are all commonly used for genetic marker data (Autrique et al, 1996; Parsons et al, 1997; Riek et al, 2001) based on the presence or absence of the alleles determining the SSR diversity.

Polymorphic information content (PIC) values will be calculated for each microsatellite, based on allele frequency. This will enable a relative value (between 0 and 1) to be given to each locus based on the diversity it reveals according to the number of detectable alleles and their frequency. Markers with high values will be used to distinguish varieties, and those with low values indicate rare alleles. Diversity indices will be calculated for selected subsets of the germplasm based upon average number of alleles, average number of polymorphic alleles, percentage of polymorphic loci, and percentage of polymorphic alleles.

Two multivariate methods (phenetic classificatory and ordination) have been widely used, both alone and in combination (Rohlf, 1992), to study patterns of genetic diversity expressed in multidimensional space defined by the marker character to assess diversity within crop germplasm. Principal Component Analysis (PCA) and cluster analysis have been used to visualise diversity in germplasm across a range of agromorphological characters or molecular marker bands, and to separate geographical or ecogeographic patterns of diversity (Crossa et al, 1995; Virk et al, 1995) . On the basis of previous work on rice accessions, we anticipate that general hierarchical agglomerative cluster analysis using Ward’s linkage will be used for the agro-morphological traits, but other cluster analysis methods will also be tested. PCA will be carried out using different combinations of the measured agro-morphological and molecular marker trait datasets (i.e. either separate or combined) both within and across sampling sites.

Agreement between agromorphological and molecular variation will be measured using Mantel’s test (Mantel, 1967) to compare the genetic distance matrix with a taxonomic distance matrix based on molecular and agro-morphological traits for all combinations within and between sampling sites. The test yields a product-moment correlation that measures the relatedness between the two matrices.

Discriminant function analysis will be used to assess the conformity of accessions to the names given to the accessions by farmers using various pre-determined groupings such as the agromorphological clusters as the membership groups. This will allow an analysis of the usefulness of farmers’ names as an initial means of describing the extent of diversity.

The endpoints of the workpackage will be a better understanding of the use of molecular markers in describing diversity in rice bean and a comparison to agromorphological estimates. The identification of polymorphic SSR markers will be of great value in any future mapping work and synteny between legume species may allow the accelerated application of these markers in marker-assisted selection breeding schemes.